Normalizes input data according to cell number or \(\mu\)g of protein. It assumes your data is background normalized.
Arguments
- seahorse_rates
The seahorse rates table read by the
read_data()function.- norm_csv
A csv file with the experimental groups in column 1 and cell count or \(\mu\)g of protein in column 2. Headers are ignored.
Details
This normalization is distinct from the background normalization done by the
Wave software. If the data are not background normalized, read_data() will
output a warning showing rows with OCR, ECAR and PER values greater than 0.
Examples
rep_list <- system.file("extdata", package = "ceas") |>
list.files(pattern = "*.xlsx", full.names = TRUE)
norm_csv <- system.file("extdata", package = "ceas") |>
list.files(pattern = "norm.csv", full.names = TRUE)
read.csv(norm_csv)
#> exp_group measure
#> 1 Group_1 30000
#> 2 Group_2 30000
#> 3 Group_3 5000
#> 4 Group_4 5000
seahorse_rates <- read_data(rep_list, sheet = 2)
head(seahorse_rates, n = 10)
#> Measurement Well Time OCR ECAR PER exp_group
#> <num> <char> <num> <num> <num> <num> <char>
#> 1: 1 A01 1.304765 0.0000 0.00000 0.0000 Background
#> 2: 1 A02 1.304765 305.2426 30.64529 334.4771 Group_1
#> 3: 1 A03 1.304765 307.9862 33.27668 358.4754 Group_1
#> 4: 1 A04 1.304765 339.3399 49.17751 503.4910 Group_2
#> 5: 1 A05 1.304765 321.9398 47.94602 492.2597 Group_2
#> 6: 1 A06 1.304765 323.7962 46.84232 482.1940 Group_2
#> 7: 1 A07 1.304765 379.1455 46.81741 481.9668 Group_3
#> 8: 1 A08 1.304765 391.1478 50.14648 512.3280 Group_3
#> 9: 1 A09 1.304765 393.4523 52.54649 534.2160 Group_3
#> 10: 1 A10 1.304765 217.0543 29.11793 320.5476 Group_4
#> assay_type replicate
#> <char> <int>
#> 1: <NA> 1
#> 2: MITO 1
#> 3: MITO 1
#> 4: MITO 1
#> 5: MITO 1
#> 6: MITO 1
#> 7: MITO 1
#> 8: MITO 1
#> 9: MITO 1
#> 10: MITO 1
seahorse_rates.normalized <- normalize(seahorse_rates, norm_csv)
head(seahorse_rates.normalized, n = 10)
#> Measurement Well Time OCR ECAR PER exp_group
#> <num> <char> <num> <num> <num> <num> <char>
#> 1: 1 A01 1.304765 0.00000 0.000000 0.00000 Background
#> 2: 1 A02 1.304765 50.87376 5.107549 55.74619 Group_1
#> 3: 1 A03 1.304765 51.33103 5.546114 59.74590 Group_1
#> 4: 1 A04 1.304765 56.55665 8.196252 83.91516 Group_2
#> 5: 1 A05 1.304765 53.65663 7.991003 82.04329 Group_2
#> 6: 1 A06 1.304765 53.96603 7.807053 80.36566 Group_2
#> 7: 1 A07 1.304765 379.14553 46.817412 481.96685 Group_3
#> 8: 1 A08 1.304765 391.14776 50.146484 512.32798 Group_3
#> 9: 1 A09 1.304765 393.45230 52.546486 534.21600 Group_3
#> 10: 1 A10 1.304765 217.05432 29.117934 320.54760 Group_4
#> assay_type replicate
#> <char> <int>
#> 1: <NA> 1
#> 2: MITO 1
#> 3: MITO 1
#> 4: MITO 1
#> 5: MITO 1
#> 6: MITO 1
#> 7: MITO 1
#> 8: MITO 1
#> 9: MITO 1
#> 10: MITO 1